Lane correction system, lane correction apparatus and method of correcting lane

ABSTRACT

The embodiment provides a method of correcting a lane. The method includes receiving first lane information detected by a lane departure warning system; comparing the received first lane information with previously stored second lane information to identify a degree of variation of a lane as a function for time; sensing whether a fault detection of the received first lane information exists according to the identified degree of variation of the lane; correcting the received first lane information when the fault detection of the first lane information is sensed; and transmitting the corrected lane information to the lane departure warning system.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit under 35 U.S.C. §119 of KoreanPatent Application No. 10-2012-0097058, filed Sep. 3, 2012, which ishereby incorporated by reference in its entirety.

BACKGROUND

The embodiment relates to a lane correction apparatus, and moreparticularly, to a lane correction system which can correct abnormallyvaried lane information detected by a Lane Departure Warning System(LDWS), and a lane correction apparatus and a method of correcting alane thereof.

Recently, as performance of computer hardware has been developed,computer vision and image processing technologies have been rapidlydeveloped, so that high resolution video data can be analyzed andprocessed in real time.

Research and studies for applying such a computer vision to a vehiclehave been actively done to reduce a traffic accident rate. In addition,research for an intelligent vehicle has been actively performed inconnection with high-technology industry of 21^(st) Century.

Further, interest in the image processing application in the generalvehicle field as well as the intelligent vehicle field has beenincreased.

A vehicle black box, which has been recently released to market,includes an impact sensor and an image sensor and stores images takenbefore and after traffic accident occurs. The black box may be utilizedas evidence for determining one's mistake. As the demand for the vehicleblack box has been increased, providers have exerted an effort toadditionally provide functions related to safety, such as a lanedeparture detection and warning, to the vehicle black box.

Meanwhile, vehicles are driven at high speed in well-paved lanes such aslanes in South Korea. For using the intelligent vehicle in real life,the process of exact computation is required without performancedegradation at high driving speed. Thus, optimization of softwarealgorithm has been requested as well as the development of hardware.

Many studies related to lane recognition have been pursued in the insideand the outside of the country. Typical schemes for recognizing a laneinclude a scheme of using the Hough transformation, a scheme of using ahistogram, and a scheme of using edge connection information.

First, the scheme of using the Hough transformation is a normal lanedetection scheme. The Hough transformation, which is generally used inthe computer vision and image processing fields, detects an object whichcan be modeled by using a polynomial expression existing in an image.The Hough transformation may obtain an excellent result and represent asuperior feature against noise.

According to a method applied to a lane recognition algorithm byutilizing a scheme of detecting a straight line using the Houghtransformation, iterative binarization using an average value isperformed based on the fact that a lane has a brightness valuedistinctly distinguished from that a road area in a rod image.

In order to detect a lane in a binarization image, the edges aredetected by using the Sobel operator and thinning is performed forreducing an amount of Hough transformation calculations. Then, the pixeldomain is converted into the parameter domain through the Houghtransformation, so several candidate points for the straight lines arecalculated near a coordinate in which a lane exists.

The pixels of the accumulated candidate points for the straight linesare added up and the maximum value thereof is detected to select onestraight line on the pixel domain to recognize the lane.

Second, the scheme of recognizing a lane through a histogram calculatesa histogram of a gray level in order to sense a lane in a road image.After forming a narrow horizontal band in the road image, the image isscanned from the bottom to the top thereof and calculates histograms ofcorresponding bands every scanning step for the thresholding to themaximum value. Thus, the lane is recognized by combining the bandsubject to the thresholding and the binary image includes all of thelanes and other objects.

Next, features are extracted from the binary image which is dividedbased on histograms, by using various information including an averageangle in corporation with a vanishing point of each pixel of an object,a center of an object, a size of an object, a maximum width of anobject, and a y-coordinate at which a maximum width and a maximum valueof an object are located.

The extracted features are clearly classified through a decision treeand the candidates that finally remain are detected as the lane througha procedure of analyzing a relationship between the road and the lane.

Third, the scheme of recognizing a lane through edge connectinginformation uses a clustering scheme. According to the scheme ofrecognizing a lane through edge connecting information, the imageinformation acquired through a camera is divided into a left image and aright image and then, clustering is performed by using edge pixels ofthe lane extracted through the Sobel operation.

The clustering procedure first selects an edge pixel serving as astarting point and designates ID. Then, a pixel of which the distancefrom the starting point to the pixel is less than the length of 2 pixelsis found among edge pixels near the starting point as the edge pixel,and the same ID of that of the starting point is assigned to the foundpixel. Each sub-pixel to which the ID is assigned becomes the startingpoint, so sub-pixels thereof are again found through the same scheme asdescribed above.

If the edge pixel does not exist in the range of two pixels, anotherpixel to which any IDs are not assigned is found and then, after a newID is assigned thereto, the same procedure as described above isrepeated. If the clustering is completed, the minimum and maximum yvalues of each cluster and x-coordinate value corresponding to the yvalues are obtained. The two coordinate values are used to obtainstraight line equations of each cluster and an extending line iscalculated through the equations. In order to measure a distance betweenthe clusters, two points about two y values which are equal to eachother are calculated through the each cluster equation, therebyobtaining the difference with respect to the widths of lanes. The twoclusters having minimum difference values are recognized as the lane.

However, since the above scheme of detecting a lane does not provide afunction for correcting detected lane information, when a fault lane isdetected, exact information cannot be provided to a user, so the usermay feel inconvenienced.

That is, as described above, in a case of a lane departure warningsystem based on an image, the fault detection is instantly increasedunder some circumstances, such as abrasion of the lane, complexsituation of a city or complex noise caused by a sign board.

BRIEF SUMMARY

The embodiment provides a lane correction system, a lane correctionapparatus and a method for correcting a lane thereof to correct abnormallane information detected by a lane departure warning system.

Further, the embodiment provides a lane correction apparatus and amethod for correcting a lane thereof to correct lane information basedon a variation of lane information as a function for time, therebyimproving the performance of the lane departure warning system andproviding more correct information to a user.

The technical tasks which will be achieved in the proposed embodimentsare not limited to above, and other technical tasks, which are notmentioned, will be apparently understood to those skilled in the art.

According to an embodiment, there is provided a lane correction systemincluding an image acquisition apparatus for acquiring a forward imagein front of a vehicle; a lane detection apparatus for receiving theforward image from the image acquisition apparatus to detect laneinformation based on the forward image; and a lane correction apparatusfor receiving first lane information detected at a present time from thelane detection apparatus and for sensing whether a fault detection ofthe first lane information exists by comparing the first laneinformation with second lane information which is previously stored,wherein the lane correction apparatus corrects the first laneinformation when the fault detection is sensed in the first laneinformation and transfers the corrected first lane information to thelane detection apparatus.

According to an embodiment, there is provided a lane correctionapparatus including a lane information transceiving unit connected to alane departure warning system for receiving first lane informationdetected by the lane departure warning system; a lane feature generatingunit for comparing the first lane information received at a present timepoint through the lane information transceiving unit with second laneinformation previously stored in order to generate a feature of thefirst lane information according to a comparison result; a lane faultdetection sensing unit for identifying a degree of variation of a lanevarying as a function for time based on the feature of the first laneinformation generated through the lane feature generating unit, and forsensing whether a fault detection of the first lane information existsbased on the identified degree of variation; and a lane informationcorrecting unit for correcting an fault-detected first lane informationwhen the fault detection of the first lane information is sensed throughthe lane fault detection sensing unit, and for transferring thecorrected first lane information to the lane departure warning system.

According to an embodiment, there is provided a method of correcting alane, the method including receiving first lane information detected bya lane departure warning system; comparing the received first laneinformation with previously stored second lane information to identify adegree of variation of a lane as a function for time; sensing whether afault detection of the received first lane information exists accordingto the identified degree of variation of the lane; correcting thereceived first lane information when the fault detection of the firstlane information is sensed; and transmitting the corrected laneinformation to the lane departure warning system.

As described above, according to the embodiment, the instant faultdetection about the lane information is found by using the previous laneinformation, and thus, the fault-detected lane information is correctedso that the situation, in which the fault detection is instantlyincreased under great noise environment or due to the faded lane, may besolved. Thus, the reliability of the lane departure warning system maybe improved and user's convenience may be increased

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a view illustrating a lane correction system according to theembodiment;

FIG. 2 is a view showing a detailed configuration of a lane detectionapparatus depicted in FIG. 1;

FIG. 3 is a view showing a detailed configuration of a lane correctionapparatus depicted in FIG. 1;

FIG. 4 is a view illustrating a method of detecting lane informationaccording to the embodiment; and

FIGS. 5 to 9 are flowcharts illustrating by steps a method of correctinga lane.

DETAILED DESCRIPTION

Hereinafter, a preferable embodiment will be described in detail withreference to the accompanying drawings.

Hereinafter, the embodiment will be described in detail with referenceto the accompanying drawings.

FIG. 1 is a view illustrating a lane correction system according to theembodiment.

Referring to FIG. 1, the lane correction system 100 includes an imageacquisition apparatus, a lane detection apparatus 200 and a lanecorrection apparatus 300.

The image acquisition apparatus 100 is installed in front of a vehicleto acquire a forward image in front of the vehicle.

The lane detection apparatus 200 receives the image acquired by theimage acquisition apparatus 100 and performs a function for recognizingvarious information about a lane of a road at which the vehicle iscurrently located based on the image.

For example, the information about the lane may include a lane equation,a lane type (for example, a solid line or a dotted line), a lanecurvature, a lane color, and a lane departure situation.

The lane correction apparatus 300 receives various information about thelane recognized by the lane detection apparatus 200 and identifies adegree of a variation of the lane as a function for time based on theinformation. The lane correction apparatus 300 corrects the informationabout the lane and then, transfers the corrected lane information to thelane detection apparatus 200.

In more detail, the image acquisition apparatus 100 is installed at thefront of the vehicle, and performs the function for acquiring a colorimage of a lane in the vehicle forward direction.

The image acquisition apparatus 100 transfers the acquired color imageto the lane detection apparatus 200.

The image acquisition apparatus 100 may include a lens having a largeangle-of-view such as a wide angle lens or a fisheye lens and a pinholecamera.

The image acquisition apparatus 100 may acquire a 2D image of a 3Dsubject through the lens having a large angle-of-view in the range of60° to 120°.

The lane detection apparatus 200 analyzes the color image to recognizethe lane included in the color image, so that the lane information isacquired.

FIG. 2 is a detailed view showing the lane detection apparatus depictedin FIG. 2.

Referring to FIG. 2, the lane detection apparatus 200 includes an imagepreprocessing unit 210, an initial lane detecting unit 220, a lanetracking unit 230, a lane type recognizing unit 240, a lane colorrecognizing unit 250, a storage unit 260, and a control unit 270.

The image preprocessing unit 210 receives a forward image in front ofthe vehicle (color image) acquired through the image acquisitionapparatus 100, calculates an integral image of the forward image infront of the vehicle, and outputs the integral image.

The initial lane detecting unit 220 may detect an initial lane equationusing the integral image input from the image preprocessing unit 210.

The lane tracking unit 230 may obtain a new lane equation using aprevious lane equation if the previous lane equation exists.

The lane type recognizing unit 240 may perform a function forrecognizing whether a lane is a solid line or a dotted line.

The lane color recognizing unit 250 may perform a function forrecognizing whether a color of a lane is white, yellow or blue, suchthat the lane color recognizing unit 250 may obtain information aboutwhether a lane is a center line or a dedicated bus lane.

The storage unit 260 stores various information and data needed tooperate the lane detection apparatus 200 and performs a function forproviding the stored information or data according to requests of eachcomponent.

The storage unit 260 may include a storage medium having at least one ofa flash memory type, a hard disk type, a multimedia card micro type, acard type of memory (for example, SD or XD memory), RAM, and EEPROM.

The control unit 270 controls overall operations of the lane detectionapparatus 200.

Specifically, the control unit 270 controls such that variousinformation about the lane is exactly detected and recognized.

Further, the control unit 270 senses whether a collision occurs based onvarious information about the detected and recognized lane, andgenerates a warning signal according to the sensing result.

Hereinafter, the above elements will be described in more detail.

The image preprocessing unit 210 receives the forward image in front ofthe vehicle. The image preprocessing unit 210 selects the G-channelcomponent of color components of the received forward image, calculatesan integral image of the G channel component and outputs the integralimage. Here, the reason why the G-channel component is selected isbecause the discrimination power of G-channel component with respect toa lane in a tunnel at night is greater than those of the other channelcomponents. Thus, although the R or B channel component is selectablefrom the image to perform the function that will be described below, itis preferable to use the G channel image.

Meanwhile, the image preprocessing unit 210 may perform an additionalfunction for correcting the image when the distortion or error occurs inthe received image.

The control unit 270 determines whether a previous lane equation existsto detect an initial lane equation by controlling the initial lanedetecting unit 200 according to the determination result.

In detail, if a valid previous lane equation does not exist, the initiallane detecting unit 220 detects the initial lane equation by using theintegral image of the G channel calculated by the image preprocessingunit 210.

That is, the initial lane detecting unit 220 calculates a subject regionfor the lane detection from the integral image input from the imagepreprocessing unit 210. The subject region for the lane detection may beobtained from the image region corresponding to a rectangular area inthe range of 3 m to 30 m from the center of the front of a vehicle (forexample, the center of a vehicle hood) in a forward direction of thevehicle and in the range of −4 m to 4 m in a left and right directionabout the center.

Next, the initial lane detecting unit 220 extracts candidate points forthe lane from the lane detection subject region. For example, theinitial lane detecting unit 220 extracts the lane candidate points byusing a lane candidate point template having a continuous step functionat the same width. That is, if the value obtained by performing theconvolution of the integral image and the lane candidate points in thesubject area for the detection of the lane is equal to a predeterminedthreshold value or above, the initial lane detecting unit 220 mayextract the corresponding coordinate as the lane candidate point.

To this end, the initial lane detecting unit 220 calculates the width ofthe lane candidate point template in which the width of the lanecandidate point template may be set in correspondence with the width ofthe painted lane of a road.

When the lane candidate point is obtained, the initial lane detectingunit 220 performs the clustering of the lane candidate point andcalculates the lane equation with respect to the extracted lanecandidate clusters.

Meanwhile, if a previous lane information exists, the control unit 270controls the lane tracking unit 230 to track a new lane equation basedon the previous lane equation.

First, when the previous lane information exists, the lane tracking unit230 sets a region of interest at the previous lane and extracts the lanecandidate point from the region of interest. The region of interest maybe set within the range of 60 cm to the left and right from the point onthe previous lane equation. The region of interest may be suitably setby a user by taking a calculation speed and an accuracy intoconsideration.

The method of extracting the lane candidate point in the region ofinterest may use the lane candidate point template as in the initiallane extraction.

Meanwhile, the lane candidate point placed at the rightmost position inthe region of interest of a left lane may be selected as therepresentative candidate point and the lane candidate point placed atthe leftmost position in the region of interest of a right lane mayselected as the representative candidate point. In this case, when thelane diverges into two stands, it is possible to select the lanecandidate point on the original lane other than the candidate point onthe new lane, so it is possible to continuously track the current laneeven if one lane in the region of interest is divided into two lanes.

Next, the lane tracking unit 230 selects a valid lane candidate pointfrom the extracted lane candidate points by using a RANdom SampleConcensus (RANSAC) algorithm. The RANSAC algorithm is for effectivelyremoving an outlier from a model fitting. The RANSAC algorithm selectsonly the inlier from the extracted left and right lane candidate pointsby using a hyperbola equation serving as the fitting model. An inlierthreshold used in the RANSAC algorithm may be 20 cm (which is one lanewidth and must be converted into a distance in image coordinates byusing a matrix (PT)) and the number of repeating times may be previouslyset. For example, the number of repeating times may be set as 100 timesby taking a system load into consideration. The following is a procedurefor selecting the inlier corresponding to the valid lane candidate pointby using the RANSAC algorithm.

Then, the lane tracking unit 230 updates a new lane equation by applyingKalman filter while utilizing the valid lane candidate point selectedthrough the RANSAC algorithm as a measurement. Meanwhile, if the numberof valid lane candidate points selected from the left or right region ofinterest is less than 10, the valid lane candidate points in the regionof interest are not included in the measurement. If the number of validlane candidate points selected from the left and right regions ofinterest is less than 10, the update process is not performed and it isdetermined that there is no lane equation.

Meanwhile, the lane candidate point may not exist in the region ofinterest due to a vibration caused when driving a vehicle on a road, atemporary disconnection of a lane or a lighting variation. As a schemefor overcoming the above problems, an Extended Kalman Filtering (EKF)scheme, which may track a non-linear equation and is robust to Gaussiannoise, is preferably used to track a lane.

If the lane tracking is completed, the lane type recognizing unit 240may determine whether the lane is a dotted line or a solid line andcalculate a lane curvature. The recognition for the dotted line or thesolid line is performed as follows. First, it is recognized whether thelane is a dotted line or a solid line by using a pixel value on the lanelocated within the range of 5 m to 20 m in the lane equation of theforward image in front of the vehicle and pixel values near the lane.That is, a G channel intensity is scanned with 20 pixels located to theleft and right about a point sampled at a predetermined interval (forexample, 1 m) on each lane. A point, in which a difference between themaximum value and the minimum value exceeds a preset threshold value, isregarded as a point on the lane, and a point, in which the differencebetween the maximum value and the minimum value is less than the presetthreshold value, is regarded as a point on a road area where the lane iscut. The ratio of the number of points on the lane is calculated basedon the total number of points in the region corresponding to 5 m to 20 min front of the vehicle. If the ratio exceeds 0.9, the lane is regardedas the sold line, and the ratio is less than 0.9, the lane is regardedas the dot line.

The curvature of the lane may be calculated with respect to the lanelocated within the range of 15 m to 30 m from the detected lane.

The lane color recognizing unit 250 may recognize the color of the lane.

Although the scheme of obtaining various information through the lanedetection has been described above, this is illustrative purpose onlyand the scheme of detecting the lane may be modified within thetechnology generally known in the art.

That is, as the scheme of detecting and recognizing the lane, one of ascheme of using the well-known Hough transformation, a scheme of using ahistogram, and a scheme of using edge connection information may beused.

FIG. 3 is a view showing a detailed configuration of the lane correctionapparatus depicted in FIG. 1.

Referring to FIG. 3, the lane correction apparatus 300 includes a laneinformation transceiving unit 310, a reference lane informationgenerating unit 320, a lane feature generating unit 330, a lane faultdetection sensing unit 340, and a lane information correcting unit 350.

The lane information transceiving unit 310 is connected to the lanedetection apparatus 200 to receive lane information detected through thelane detection apparatus 200.

The lane information transceiving unit 310 transmits error-correctedlane information to the lane detection apparatus 200 when an erroroccurs in the received lane information and transmits the received laneinformation to the lane detection apparatus 200 when the normal laneinformation is received.

In the description below, ‘t’ denotes lane information detected at apresent time point and ‘t−1’ denotes lane information detected in thepast, that is, reference lane information.

The reference lane information generating unit 320 generates thereference lane information by using the lane information receivedthrough the lane information transceiving unit 310.

The reference lane information generating unit 320 analyzes the laneinformation received for a predetermined time, and when informationhaving a similar pattern is continuously input (if it satisfies a logicformula of the lane fault detection sensing unit later), the referencelane information generating unit 320 registers the lane information asthe reference lane information.

The reference lane information may be expressed as following Equation 1:

L _(left,t−1)(x)=a _(left,t−1) x ² +b _(left,t−1) x+c _(left,t−1)

L _(right,t−1)(x)=a _(right,t−1) x ² +b _(right,t−1) x+c _(right,t−1)  [Equation 1]

In Equation 1, L_(left,t−1)(x) is the reference lane information for aleft lane generated by using the previous lane information, andL_(right,t−1)(x) is the reference lane information for a right lanegenerated by using the previous lane information.

The lane feature generating unit 330 extracts features of the referencelane information and the current lane information received at thepresent time point through the lane information transceiving unit 310.

The received current lane information may be expressed as Equation 2:

L _(left,t)(x)=a _(left,t) x ² +b _(left,t) x+c _(left,t)

L _(right,t)(x)=a _(right,t) x ² +b _(right,t) x+c _(right,t)  [Equation 2]

In Equation 2, L_(left,t)(x) is the lane information for a left lanegenerated by using the current receive lane information, andL_(right,t)(x) is reference the lane information for a right lanecurrently received.

Further, the feature of the lane information may include a degree ofvariation of a lane angle, a degree of variation of an upper lane widthand a degree of variation of a lower lane width as a function of timerecognized based on the lane angle, the upper lane width and the lowerlane width.

To this end, the lane feature generating unit 330 generates a left laneangle and a right lane angle corresponding to the reference laneinformation and the current lane information and registers them.

Then, the lane feature generating unit 330 identifies the variationvalues of the left lane angle corresponding to the registered currentlane information and the left lane angle corresponding to the referencelane information.

The variation value of the left lane angle may be obtained from Equation3:

θ_(left,diff) =a tan(b _(left,t))−a tan(b _(left,t−1))   [Equation 3]

In addition, the lane feature generating unit 330 identifies thevariation values of the right lane angle corresponding to the registeredcurrent lane information and the right lane angle corresponding to thereference lane information.

The variation value of the right lane angle may be obtained fromEquation 4:

θ_(right,diff) =a tan(b _(right,t))−a tan(b _(right,t−1))   [Equation 4]

The lane feature generating unit 330 identifies the top end width of thecurrent lane based on the current lane information and the top end widthof the reference lane based on the reference lane information.

The top end width may be obtained from Equation 5:

W _(top) =L _(right)(x _(top))−L _(left)(x _(top))   [Equation 5]

In addition, the lane feature generating unit 330 identifies the bottomend width of the current lane based on the current lane information andthe bottom end width of the reference lane based on the reference laneinformation.

The bottom end width may be obtained from Equation 6:

W _(bottom) =L _(right)(x _(bottom))−L _(left)(x _(bottom))   [Equation6]

The top end location X_(top) and the bottom end location X_(bottom) aredefined as follows:

${where},\left\{ \begin{matrix}{x_{top} = {\left( {1 - 0.38} \right) \times {height}\mspace{14mu} {of}\mspace{14mu} {image}}} \\{x_{bottom}\mspace{14mu} {is}\mspace{14mu} {height}\mspace{14mu} {of}\mspace{14mu} {image}}\end{matrix} \right.$

Then, the lane feature generating unit 330 identifies the variationvalues of the top end widths of the identified current lane and thereference lane.

The variation value of the top end width may be obtained from Equation7:

W _(top,diff)(x ₁)=W _(t)(x ₁)−W _(t−1)(x ₁)   [Equation 7]

Then, the lane feature generating unit 330 identifies the variationvalues of the bottom end widths of the identified current lane and thereference lane.

The variation value of the bottom end width may be obtained fromEquation 8:

W _(bottom,diff)(x ₂)=W _(t)(x ₂)−W _(t−1)(x ₂)   [Equation 8]

As described above, the lane feature generating unit 330 generates thelane feature such as variation values of the left lane angle, right laneangle, and top end lane width and bottom end lane width, and outputs thelane feature.

The lane fault detection sensing unit 340 senses a fault detection ofthe current lane information based on the lane feature generated throughthe lane feature generating unit 330.

The fault detection may be sensed as following logic equations.

First, the lane fault detection sensing unit 340 compares the anglevariation vale of the left lane with a predetermined threshold valueaccording to Logic equation 1:

$\begin{matrix}{F_{{left},\theta} = \left\{ \begin{matrix}{1,} & {\theta_{{left},{diff}} \geq \theta_{th}} \\{0,} & {otherewise}\end{matrix} \right.} & \left\lbrack {{Logic}\mspace{14mu} {equation}\mspace{14mu} 1} \right\rbrack\end{matrix}$

Further, the lane fault detection sensing unit 340 compares the anglevariation vale of the right lane with a predetermined threshold valueaccording to Logic equation 2:

$\begin{matrix}{F_{{right},\theta} = \left\{ \begin{matrix}{1,} & {\theta_{{right},{diff}} \geq \theta_{th}} \\{0,} & {otherewise}\end{matrix} \right.} & \left\lbrack {{Logic}\mspace{14mu} {equation}\mspace{14mu} 2} \right\rbrack\end{matrix}$

The lane fault detection sensing unit 340 compares the width variationvale of the top end lane with a predetermined threshold value accordingto Logic equation 3:

$\begin{matrix}{F_{TopW} = \left\{ \begin{matrix}{1,} & {\theta_{diff} \geq \theta_{th}} \\{0,} & {otherewise}\end{matrix} \right.} & \left\lbrack {{Logic}\mspace{14mu} {equation}\mspace{14mu} 3} \right\rbrack\end{matrix}$

The lane fault detection sensing unit 340 compares the width variationvale of the bottom end lane with a predetermined threshold valueaccording to Logic equation 4:

$\begin{matrix}{F_{BotW} = \left\{ \begin{matrix}{1,} & {\theta_{diff} \geq \theta_{th}} \\{0,} & {otherewise}\end{matrix} \right.} & \left\lbrack {{Logic}\mspace{14mu} {equation}\mspace{14mu} 4} \right\rbrack\end{matrix}$

Further, the lane fault detection sensing unit 340 identifies whether areference lane exists based on Logic equation 5:

$\begin{matrix}{F_{Prev} = \left\{ \begin{matrix}{1,} & {L_{t - 1}\mspace{14mu} {is}\mspace{14mu} {updated}} \\{0,} & {otherewise}\end{matrix} \right.} & \left\lbrack {{Logic}\mspace{14mu} {equation}\mspace{14mu} 5} \right\rbrack\end{matrix}$

Then, the lane fault detection sensing unit 340 applies the result valeobtained from Logic equations 1 to 5 to following Logic equation 6:

F _(prediction) =F _(prev)·(F _(right,θ) +F _(left,θ))·(F _(TopW) +F_(BotW))   [Logic equation 6]

In Logic equation 6, ‘+’ signifies that the result becomes ‘1’ when atleast one of two conditions is satisfied, and ‘·’ signifies that theresult becomes ‘1’ when both conditions are satisfied.

The lane fault detection sensing unit 340 applies the result value ofLogic equation 6 to following Logic equation 7, so that the faultdetection of the lane information detected at the present time pointoccurs:

$\begin{matrix}{L_{t} = \left\{ \begin{matrix}{L_{t - 1},} & {F_{prediction} = 1} \\{L_{t},} & {otherewise}\end{matrix} \right.} & \left\lbrack {{Logic}\mspace{14mu} {equation}\mspace{14mu} 7} \right\rbrack\end{matrix}$

In Logic equation 7, if the result value of Logic equation 7 is ‘1’, itsignifies that an error exists in the lane information detected at thepresent time point, and if the result value of Logic equation 7 is ‘0’,it signifies that the lane information detected at the present timepoint is correct.

The lane information correcting unit 350 receives the lane faultdetection sensing result sensed through the lane fault detection sensingunit 340, and generates lane information to be transmitted to the lanedetection apparatus 200 according to the received sensing result.

If the lane information is normally detected, the lane informationcorrecting unit 350 transmits the lane information itself received fromthe lane detection apparatus 200. And, if the lane information isfault-detected, the lane information correcting unit 350 transmits thegenerated reference lane information (past lane information) to the lanedetection apparatus 200.

Meanwhile, the above sensing of the lane fault detection is performedbased on the reference lane information according to the previousdetected lane information.

If an error exists in the reference lane information, the lane faultdetection may be continuously sensed.

Thus, if the lane fault detection is repeatedly sensed the predeterminednumber of times or more by the lane fault detection sensing unit 340,the reference lane generating unit 320 deletes the previously generatedreference lane information and regenerates new reference laneinformation by using the lane information input at the present timepoint.

Further, if the normal lane information is continuously detected by thelane detection apparatus 200, the lane fault detection sensing unit 340updates the reference lane information by using the detected laneinformation.

As described above, according to the embodiment, the instant situationof the fault detection about the lane information is found by using theprevious lane information, and thus, the fault-detected lane informationis corrected so that the situation, in which the fault detection isinstantly increased under a great noise environment or in a faded lane,may be solved. Thus, the reliability of the lane departure warningsystem may be improved and user convenience may be increased.

FIG. 4 is a view illustrating a method of detecting lane informationaccording to the embodiment.

Referring to FIG. 4, in step S101, the lane detection apparatus 200receives the forward image in front of the vehicle acquired through theimage acquisition apparatus 200.

In step S102, the lane detection apparatus 200 generates the integralimage of the input image.

In step S103, as the integral image is generated, the lane detectionapparatus 200 determines whether a previous lane equation exists.

In step S104, if the previous lane equation exists in step S103, acurrent lane equation is tracked by using the previous lane equation.

In step S105, if the previous lane equation does not exist in step S103,an initial lane equation is detected.

Then, in step S106, a type and a curvature of the current lane arecalculated by using the tracked current lane equation or the detectedinitial lane equation.

In step S107, the lane detection apparatus 200 recognizes a color of alane.

Then, in step S108, the lane information about the current lanerecognized above is transmitted to the lane correction apparatus 300.

FIGS. 5 to 9 are flowcharts illustrating by steps a method of correctinga lane.

First, referring to FIG. 5, in step S201, the lane correction apparatus300 receives the lane information detected from the lane detectionapparatus 200.

In step S202, if the lane information is received, the lane correctionapparatus 300 determines whether the reference lane informationgenerated by using the previously received lane information exists.

In step S203, if the reference lane information does not exist in stepS202, the lane correction apparatus 300 receives the lane informationdetected through the lane detection apparatus 200 during a predeterminedtime of ‘t’ and generates the reference lane information by using thereceived lane information.

The procedure of generating the reference lane information (S203) willbe described with reference to FIG. 6.

Then, in step S204, the lane correction apparatus 300 detects the lanefeature by using the reference lane information and the currentlyreceived lane information.

The lane feature is detected based on the left lane angle, the rightlane angle, the top end lane width and the bottom end lane width whichcorrespond to the reference lane information and the currently receivedlane information, respectively.

That is, the lane feature signifies the variation degree of the laneinformation detected according to time. The variation degree of the laneinformation may include the variation values of the left lane angle, theright lane angle, the top end lane width and the bottom end lane width.

The variation value of the left lane angle is the difference valuebetween the left lane angle corresponding to the currently received laneinformation and the left lane angle corresponding to the reference laneinformation.

The variation value of the right lane angle is the difference valuebetween the right lane angle corresponding to the currently receivedlane information and the right lane angle corresponding to the referencelane information.

The variation value of the top end lane width is the difference valuebetween the top end lane width corresponding to the currently receivedlane information and the top end lane width corresponding to thereference lane information.

The variation value of the bottom end lane width is the difference valuebetween the bottom end lane width corresponding to the currentlyreceived lane information and the bottom end lane width corresponding tothe reference lane information.

In step S205, if the above lane features are detected, the lanecorrection apparatus 300 compares the lane features with the thresholdvalues, respectively, to identify whether the fault detection occurs inthe currently received lane information.

Then, in step S206, it is determined whether an error occurs in thedetection of the currently received lane information.

In step S207, if an error occurs in the detection of the currentlyreceived lane information in step S206, the lane correction apparatusdetermines that an error exists in the currently received laneinformation and thus, transmits the reference lane information to thelane detection apparatus 200.

In step S208, if the currently received lane information is normallydetected in step S206, the lane correction apparatus 300 transmits thecurrently received lane information itself to the lane detectionapparatus.

Hereinafter, the procedure of generating the reference lane informationwill be described.

First, in step S301, if the reference lane information does not exist,the lane correction apparatus 300 receives the lane information detectedby the lane detection apparatus 200 for the predetermined time t.

In step S302, the lane correction apparatus 300 detects the features ofthe lane information received for the predetermined time.

Then, in step S303, the lane correction apparatus 300 compares thedetected features with each other. The comparing of step S303 isperformed in the same way as that of the sensing of the fault detectiondepicted in FIG. 5.

That is, the lane correction apparatus 300 compares the features of thelane information input for the predetermined time with each other toidentify whether the input lane information is normally detected. Thismay be implemented by identifying whether the lane information having asimilar pattern is continuously detected for the predetermined time,that is, whether the degree of variation of the lane information as afunction for time is equal to the threshold value or below.

Then, in step S304, the lane correction apparatus 300 identifies whetherthe features of the lane information input for the predetermined timehas a similar pattern to each other.

In step S305, if the features of the lane information input for thepredetermined time has a similar pattern to each other in step S304, themost recently received lane information is registered as the referencelane information, and otherwise, the procedure returns to step S301.

Hereinafter, the procedure of detecting a lane feature will be describedin detail.

Referring to FIG. 7, in step S401, the lane correction apparatus 300detects an angle of a lane.

That is, the lane correction apparatus 300 detects a left lane angle anda right lane angel about a previous lane by using reference laneinformation.

Further, the lane correction apparatus 300 detects a left lane angle anda right lane angel about a current lane by using currently received laneinformation.

In step S402, the lane correction apparatus 300 detects a variationvalue of the lane angle as a function for by using the detected laneangle.

Next, in steps S403 and S404, the lane correction apparatus 300 detectsa lane width of a current lane by using currently received laneinformation and a lane width of a previous lane by using previouslyreceived lane information.

Here, the lane widths include a top end lane width of an upper portionand a bottom end lane width of a lower portion of the lane.

Then, in steps S405 and S406, the lane correction apparatus 300 detectsa variation of a lane width by using the detected lane width.

That is, the lane correction apparatus 300 detects a variation valueaccording to a difference between the top end lane width of the detectedcurrent lane and the top end lane width of the previous lane.

Further, the lane correction apparatus 300 detects a variation valueaccording to a difference between the bottom end lane width of thedetected current lane and the bottom end lane width of the previouslane.

Thus, the lane correction apparatus 300 detects the angle variationvalue and width variation value of the lane according to time.

Hereinafter, the procedure of sensing a fault detection of a lane willbe described.

Referring to FIG. 8, in step S501, the lane correction apparatus 300compares an angle variation value of a lane with a threshold value.

That is, the lane lien correction apparatus 300 first-compares an anglevariation value of a left lane with a predetermined first thresholdvalue. In addition, the lane lien correction apparatus 300second-compares an angle variation value of a right lane with apredetermined second threshold value.

Then, in step S502, the lane lien correction apparatus 300third-compares a top end width variation value of the detected lane witha predetermined third threshold value.

In step S503, the lane lien correction apparatus 300 forth-compares abottom end width variation value of the detected lane with apredetermined fourth threshold value.

In step S504, the lane correction apparatus 300 identifies whether thereference lane information exists.

In step S505, if the comparing and identifying are completed, the lanecorrection apparatus 300 outputs the result values of the first tofourth comparisons and the identifying result value.

Then, the lane correction apparatus 300 uses the first to fourthcomparison result values and the identifying result value to identifywhether a fault detection of the currently received lane informationoccurs, so that the lane correction apparatus 300 determines whether tocorrect the lane information.

Then, referring to FIG. 9, in step S601, the lane correction apparatus300 determines whether the fault detection of the currently receivedlane information is sensed.

In step S602, as the determination result, if an error exists in thedetection of the lane information, it is determined whether the faultdetection of the lane information is repeatedly sensed.

In step S603, if the fault detection of the lane information isrepeatedly sensed in step 602, the lane correction apparatus 300 resetsthe previously generated reference lane information.

In step S604, the lane correction apparatus 300 regenerates thereference lane information as shown in FIG. 6.

As described above, according to the embodiment, the instant faultdetection about the lane information is found by using the previous laneinformation, and thus, the fault-detected lane information is correctedso that the situation, in which the fault detection is instantlyincreased under great noise environment or in a faded lane, may besolved. Thus, the reliability of the lane departure warning system maybe improved and user's convenience may be increased.

Any reference in this specification to “one embodiment,” “anembodiment,” “example embodiment,” etc., means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the invention. Theappearances of such phrases in various places in the specification arenot necessarily all referring to the same embodiment. Further, when aparticular feature, structure, or characteristic is described inconnection with any embodiment, it is submitted that it is within thepurview of one skilled in the art to effect such feature, structure, orcharacteristic in connection with other ones of the embodiments.

Although embodiments have been described with reference to a number ofillustrative embodiments thereof, it should be understood that numerousother modifications and embodiments can be devised by those skilled inthe art that will fall within the spirit and scope of the principles ofthis disclosure. More particularly, various variations and modificationsare possible in the component parts and/or arrangements of the subjectcombination arrangement within the scope of the disclosure, the drawingsand the appended claims. In addition to variations and modifications inthe component parts and/or arrangements, alternative uses will also beapparent to those skilled in the art.

What is claimed is:
 1. A lane correction system comprising: an imageacquisition apparatus for acquiring a forward image in front of avehicle; a lane detection apparatus for receiving the forward image fromthe image acquisition apparatus to detect lane information based on theforward image; and a lane correction apparatus for receiving first laneinformation detected at a present time from the lane detection apparatusand for sensing whether a fault detection of the first lane informationexists by comparing the first lane information with second laneinformation which is previously stored, wherein the lane correctionapparatus corrects the first lane information when the fault detectionis sensed in the first lane information and transfers the correctedfirst lane information to the lane detection apparatus.
 2. The lanecorrection system of claim 1, wherein the second lane information isreference lane information generated based on lane informationtransferred from the lane detection apparatus during a predeterminedtime in past, and the lane correction apparatus generates the secondlane information by using the first lane information detected throughthe lane detection apparatus during a predetermined time from a presenttime point.
 3. The lane correction system of claim 1, wherein the firstlane information and the second lane information include informationabout at least one of a left lane angle, a right lane angle, a top endlane width, and a bottom end lane width.
 4. The lane correction systemof claim 3, wherein the lane correction apparatus detects a feature ofthe first lane information by using the received first and second laneinformation, and senses whether the fault detection of the first laneinformation exists by using the detected feature, and wherein thefeature includes a degree of variation of a lane as a function for time.5. The lane correction system of claim 4, wherein the feature of thelane information includes at least one of a variation value of the leftlane angle, a variation value of the right lane angle, a variation valueof the top end lane width, and a variation value of the bottom end lanewidth created based on a difference between the first lane informationand the second lane information, and wherein the lane correctionapparatus corrects the first lane information when at least one of thevariation values is greater than a predetermined threshold value.
 6. Thelane correction system of claim 3, wherein the lane correction apparatusinitiates the previously stored second lane information to regeneratethe second lane information when the fault detection of the first laneinformation is repeated for a predetermined number of times or more. 7.A lane correction apparatus comprising: a lane information transceivingunit connected to a lane departure warning system for receiving firstlane information detected by the lane departure warning system; a lanefeature generating unit for comparing the first lane informationreceived at a present time point through the lane informationtransceiving unit with second lane information previously stored inorder to generate a feature of the first lane information according to acomparison result; a lane fault detection sensing unit for identifying adegree of variation of a lane varying as a function for time based onthe feature of the first lane information generated through the lanefeature generating unit, and for sensing whether a fault detection ofthe first lane information exists based on the identified degree ofvariation; and a lane information correcting unit for correcting anfault-detected first lane information when the fault detection of thefirst lane information is sensed through the lane fault detectionsensing unit, and for transferring the corrected first lane informationto the lane departure warning system.
 8. The lane correction apparatusof claim 7, further comprising: a reference lane information generatingunit for generating the second lane information by using a plurality offirst lane information detected by the lane departure warning systemduring a predetermine time, wherein the reference lane informationgenerating unit generates the second lane information by using theplurality of first lane information when the second lane informationdoes not exist at a time point of receiving the first lane information.9. The lane correction apparatus of claim 8, wherein the reference laneinformation generating unit updates the second lane information by usinga normally detected first lane information when a normal detection ofthe first lane information is sensed.
 10. The lane correction apparatusof claim 8, wherein the first lane information and the second laneinformation include information about at least one of a left lane angle,a right lane angle, a top end lane width, and a bottom end lane width.11. The lane correction apparatus of claim 10, wherein the lane faultdetection sensing apparatus detects a feature of the first laneinformation by using the received first and second lane information, andsenses whether the fault detection of the first lane information existsby using the detected feature, and wherein the feature of the laneinformation includes at least one of a variation value of the left laneangle, a variation value of the right lane angle, a variation value ofthe top end lane width, and a variation value of the bottom end lanewidth created based on a difference between the first lane informationand the second lane information.
 12. The lane correction apparatus ofclaim 11, wherein the lane fault detection sensing unit senses the faultdetection of the first lane information when at least one of thevariation values is greater than a predetermined threshold value. 13.The lane correction apparatus of claim 11, wherein the reference lanegenerating unit initiates the previously stored second lane informationto regenerate the second lane information when the fault detection ofthe first lane information is repeated for a predetermined number oftimes or more.
 14. A method of correcting a lane, the method comprising:receiving first lane information detected by a lane departure warningsystem; comparing the received first lane information with previouslystored second lane information to identify a degree of variation of alane as a function for time; sensing whether a fault detection of thereceived first lane information exists according to the identifieddegree of variation of the lane; correcting the received first laneinformation when the fault detection of the first lane information issensed; and transmitting the corrected lane information to the lanedeparture warning system.
 15. The method of claim 14, furthercomprising: generating the second lane information by using a pluralityof first lane information detected by the lane departure warning systemduring a predetermine time.
 16. The method of claim 15, wherein thegenerating of the second lane information comprises: identifying afeature of the plurality of first lane information; determining whetherthe plurality of first lane information is normally detected based onthe identified feature; and registering the first lane informationreceived at a latest time point as the second lane information when theplurality of first lane information is normally detected.
 17. The methodof claim 15, wherein the first lane information and the second laneinformation include information about at least one of a left lane angle,a right lane angle, a top end lane width, and a bottom end lane width.18. The method of claim 15, wherein the identifying of the degree ofvariation of the lane includes identifying at least one of a variationvalue of the left lane angle, a variation value of the right lane angle,a variation value of the top end lane width, and a variation value ofthe bottom end lane width created based on a difference between thefirst lane information and the second lane information.
 19. The methodof claim 18, wherein the sensing of the fault detection includes sensingthe fault detection of the first lane information when at least one ofthe variation values is greater than a predetermined threshold value,and wherein the correcting of the received first lane informationincludes changing the first lane information into the second laneinformation when the first lane information is fault-detected.
 20. Themethod of claim 19, further comprising: initiating the previously storedsecond lane information to regenerate the second lane information whenthe fault detection of the first lane information is repeated for apredetermined number of times or more.